Self-Organizing Map with False Neighbor Degree between Neurons for Effective Self-Organization
Schlagworte: Self-Organizing Map (SOM), unsupervised learning, learning, DDC: 004 (Data processing, computer science, computer systems)
AbstractIn the real world, it is not always true that the nextdoor house is close to my house, in other words, "neighbors" are not always "true neighbors". In this study, we propose a new Self-Organizing Map (SOM) algorithm, SOM with False Neighbor degree between neurons (called FN-SOM). The behavior of FN-SOM is investigated with learning for various input data. We confirm that FN-SOM can obtain the more effective map reflecting the distribution state of input data than the conventional SOM and Growing Grid.